April 2020
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[Met-jobs] PhD studentship at CNRM and LA (Toulouse, France) on the assimilation of MTG Lightning Imager (LI) observations

From Olivier Caumont <>
To met-jobs <>
Date Thu, 9 Apr 2020 18:16:58 +0200 (CEST)

Dear all,

There is an exciting opportunity for a 3-year PhD studentship within the French National Centre for Meteorological Research (CNRM, and Laboratoire d'Aérologie (LA, in Toulouse, France, on the preparation for the assimilation of Meteosat Third Generation (MTG) Lightning Imager (LI) observations in convective-scale numerical weather prediction systems. The project is motivated by the need to improve the prediction of atmospheric deep convection (thunderstorms) and by the increasing availability of space-based geostationary lightning observations.

The studentship offers the opportunity to perform world-leading, interdisciplinary research in the fields of mesoscale meteorology, atmospheric electricity and data assimilation. A full description of the PhD project is available below.

Applications are invited from domestic and international high-quality graduates able to start their PhD studies in October 2020. The candidate must therefore hold a master's degree. The ideal candidate should have a strong background in atmospheric physics, with a particular interest in severe weather. Knowledge in atmospheric electricity and data assimilation would be a plus. The candidate should be self-motivated, with strong research potential, good programming skills, and good oral and written communication skills. A strong scientific curiosity, a good spirit of synthesis and initiative will be appreciated. The candidate must also be able to read and express him/herself in English. Some knowledge of the French language will be a plus but not necessary.

The applications including a detailed resume, an academic transcript, a letter of recommendation from the master’s degree supervisor/engineering school, and the names of two references, must be sent to Dr Olivier Caumont ( by May 6, 2020.

Project description:
Thunderstorms are among the most destructive natural phenomena, whether through their direct effects such as wind gusts, hail and lightning, or indirect effects such as flash floods resulting from heavy rainfall. These meteorological phenomena threaten not only many economic sectors such as aviation, agriculture, etc., but also human lives. The Mediterranean basin is the more concerned by this risk as it is regularly subject to 'Mediterranean episodes', which are sustained and persistent stormy rainfall events causing flash floods with tragic consequences. Among these episodes, one can mention for example the one that affected the Aude basins during the night of 14 to 15 October 2018 and whose toll amounted to 14 people dead, dozens of injured and hundreds of millions of euros of damage.

Numerical Weather Prediction (NWP) models are a key tool in anticipating such events, enabling State services and economic players to take appropriate protection measures and facilitate the management of these meteorological hazards. In principle, they make it possible to know, several hours in advance, the intensity and location of thunderstorms. However, despite the continuous increase in the realism of these NWP models, forecasting thunderstorms remains difficult. Among the reasons for this difficulty are the uncertainties in the current description of the initial state of the atmosphere that determines the future state of the atmosphere and its evolution. The process of building initial states from observations is called 'data assimilation' (DA). DA is the optimal combination of observations with a very short-term forecast (typically 1 h) acting as a background or first guess. The uncertainties in initial states are due to both a deficit of observations, including those related to thunderstorms, and limitations in current DA systems which are not able to handle observations related to cloud microphysics properly. Most operational DA systems are based on variational (Var) or ensemble techniques and are actually not able to update cloud microphysical variables. Fortunately, emerging DA systems based on ensemble-variational (EnVar) techniques make it possible to handle any prognostic variable, including those related to cloud microphysics, and thus are potential assets to make progress in the assimilation of thunderstorm observations.

The European Organisation for Meteorological Satellites (Eumetsat) will launch from 2022 onwards a series of satellites in geostationary orbit (Meteosat Third Generation, MTG) carrying Lightning Imagers (LIs). This new type of sensor has the great advantage of observing all types of lightning (cloud-to-ground and intra-cloud) flashes continuously and homogeneously, at a spatial resolution of a few kilometres, in real time and over a hemisphere covering the whole of Europe. The LI will ideally complement the observations of thunderstorms made by ground-based meteorological radars. Indeed it will allow the monitoring of thunderstorms at sea or in mountainous regions, i.e. out of the range of weather radars.

The objective of the proposed PhD thesis is to explore and evaluate the relevance of using LI observations/products to improve the forecasts of convective-scale NWP systems that use next-generation EnVar DA techniques. Here several challenges will have to be addressed.
 First, the LI observations will have to be simulated from other observations as the instrument is not yet in space. This preparatory work has been successively performed during a previous PhD thesis and a tool is available that allows the production of LI pseudo-observations from observations recorded by ground-based lightning locating systems.
 Secondly, the LI observations will have to be simulated from the NWP model in order to compare the model simulations with the observations. The potential of hybrid machine learning techniques (i.e. including physical modelling) will be explored to tackle this issue.
 Finally, a cutting-edge prototype of a hybrid four-dimensional EnVar system will be used in this work. This DA system is planned to supersede by 2021-2023 the three-dimensional variational (3D-Var) DA system currently used in the AROME NWP system of Météo-France. In addition to its ability to handle cloud microphysical variables directly, this new DA system is able to process data at a higher frequency (every 15 min against every hour with the 3D-Var system). LI data assimilation experiments will be carried out over a rather long period including significant storms in recent years in the Mediterranean region. The benefits of LI-like data assimilation will be quantitatively assessed through several experiments using or not assimilation for several cloud microphysical and dynamical parameters.

If the observations of the first LI instrument become available during the thesis, they will replace the LI pseudo-observations and a systematic comparison between LI real and LI model observations will be set up, which paves the way for the operational assimilation of LI observations.

Olivier Caumont
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France

42 av G. Coriolis
31057 TOULOUSE Cedex 01 (FRANCE)

Tel: +33 5 61 07 96 46 | Fax: +33 5 61 07 96 26
Personal webpage:

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